Robust Opponent Modeling in Real-Time Strategy Games using Bayesian Networks | ||
| Journal of AI and Data Mining | ||
| مقاله 13، دوره 7، شماره 1، فروردین 2019، صفحه 149-159 اصل مقاله (1.28 M) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2018.6932.1815 | ||
| نویسندگان | ||
| A. Torkaman1؛ R. Safabakhsh* 2 | ||
| 1Computer Engineering Department, Amirkabir University of Technology, Tehran, Iran. | ||
| 2Amirkabir University of Technology | ||
| چکیده | ||
| Opponent modeling is a key challenge in Real-Time Strategy (RTS) games as the environment is adversarial in these games, and the player cannot predict the future actions of her opponent. Additionally, the environment is partially observable due to the fog of war. In this paper, we propose an opponent model which is robust to the observation noise existing due to the fog of war. In order to cope with the uncertainty existing in these games, we design a Bayesian network whose parameters are learned from an unlabeled game-logs dataset; so it does not require a human expert’s knowledge. We evaluate our model on StarCraft which is considered as a unified test-bed in this domain. The model is compared with that proposed by Synnaeve and Bessiere. Experimental results on recorded games of human players show that the proposed model can predict the opponent’s future decisions more effectively. Using this model, it is possible to create an adaptive game intelligence algorithm applicable to RTS games, where the concept of build order (the order of building construction) exists. | ||
| کلیدواژهها | ||
| Bayesian Network؛ Opponent modeling؛ Real-Time Strategy games؛ StarCraft | ||
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آمار تعداد مشاهده مقاله: 1,861 تعداد دریافت فایل اصل مقاله: 1,060 |
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